• Corpus ID: 231951374

Multilevel calibration weighting for survey data

@inproceedings{BenMichael2021MultilevelCW,
  title={Multilevel calibration weighting for survey data},
  author={Eli Ben-Michael and Avi Feller and Erin Hartman},
  year={2021}
}
In the November 2016 U.S. presidential election, many state level public opinion polls, particularly in the Upper Midwest, incorrectly predicted the winning candidate. One leading explanation for this polling miss is that the precipitous decline in traditional polling response rates led to greater reliance on statistical methods to adjust for the corresponding bias—and that these methods failed to adjust for important interactions between key variables like education, race, and geographic… 

Figures from this paper

Kpop: A kernel balancing approach for reducing specification assumptions in survey weighting
TLDR
Kpop describes kernel balancing for population weighting (kpop), which replaces the design matrix $X$ with a kernel matrix, $\mathbf{K}$ encoding high-order information about X, and finds that good calibration on a wide range of smooth functions of $X$, without relying on the user to explicitly specify those functions.
Sensitivity Analysis for Survey Weights
Survey weighting allows researchers to account for bias in survey samples, due to unit nonresponse or convenience sampling, using measured demographic covariates. Unfortunately, in practice, it is
The Balancing Act in Causal Inference
The idea of covariate balance is at the core of causal inference. Inverse propensity weights play a central role because they are the unique set of weights that balance the covariate distributions of
Hierarchically Regularized Entropy Balancing
TLDR
This work introduces hierarchically regularized entropy balancing as an extension to entropy balancing, a reweighting method that adjusts weights for control group units to achieve covariate balance in observational studies with binary treatments and develops an open-source R package to facilitate implementation.
Some Model Assisted Estimators Using Functional Form Calibration Approach
The Model assisted estimators are approximately design unbiased, consistent and provides robustness in the case of large sample sizes. The model assisted estimators result in reduction of the design

References

SHOWING 1-10 OF 52 REFERENCES
Kpop: A kernel balancing approach for reducing specification assumptions in survey weighting
TLDR
Kpop describes kernel balancing for population weighting (kpop), which replaces the design matrix $X$ with a kernel matrix, $\mathbf{K}$ encoding high-order information about X, and finds that good calibration on a wide range of smooth functions of $X$, without relying on the user to explicitly specify those functions.
An Evaluation of the 2016 Election Polls in the United States
The 2016 presidential election was a jarring event for polling in the United States. Preelection polls fueled high-profile predictions that Hillary Clinton’s likelihood of winning the presidency was
Balancing Versus Modeling Approaches to Weighting in Practice
TLDR
The balancing approach to weighting is examined, recent methodological developments are discussed, and instances of the balancing and modeling approaches in a simulation study and an empirical study are compared.
Approximate residual balancing: debiased inference of average treatment effects in high dimensions
TLDR
A method for debiasing penalized regression adjustments to allow sparse regression methods like the lasso to be used for √n‐consistent inference of average treatment effects in high dimensional linear models.
Target Selection as Variable Selection : Using the Lasso to Select Auxiliary Vectors for the Construction of Survey Weights ∗
Survey nonresponse is a ubiquitous problem in modern survey research. As individuals have become less likely to respond to surveys there has been a simultaneous rise in highly granular data sources
Calibrating non‐probability surveys to estimated control totals using LASSO, with an application to political polling
TLDR
This work estimates voting preference for 19 elections in the US 2014 midterm elections by using large non‐probability surveys obtained from SurveyMonkey users, calibrated to estimated control totals using model‐assisted calibration combined with adaptive LASSO regression, or the estimated controlled LASSo, ECLASSO.
Variation in impacts of letters of recommendation on college admissions decisions: Approximate balancing weights for treatment effect heterogeneity in observational studies∗
Assessing treatment effect variation in observational studies is challenging because differences in estimated impacts across subgroups reflect both differences in impacts and differences in covariate
Deep Interactions with MRP: Election Turnout and Voting Patterns Among Small Electoral Subgroups
Usingmultilevelregressionandpoststratification(MRP),weestimatevoterturnoutandvotechoicewithindeeplyinteracted subgroups: subsets of the population that are defined by multiple demographic and
Stable Weights that Balance Covariates for Estimation With Incomplete Outcome Data
Weighting methods that adjust for observed covariates, such as inverse probability weighting, are widely used for causal inference and estimation with incomplete outcome data. Part of the appeal of
Improving multilevel regression and poststratification with structured priors
TLDR
This work aims to provide a new framework for specifying structured prior distributions that lead to bias reduction and variance reduction in MRP estimates in a large variety of data regimes and demonstrates their efficacy on non-representative US survey data.
...
...